Well Logging Stratigraphic Correlation Algorithm Based on Semantic Segmentation

Well logging curves serve as indicators of strata attribute changes and are frequently utilized for stratigraphic analysis and comparison. Deep learning, known for its robust feature extraction capabilities, has seen continuous adoption by scholars in the realm of well logging stratigraphic correlation tasks. Nonetheless, current deep learning algorithms often struggle to accurately capture feature changes occurring at layer boundaries within the curves. Moreover, when faced with data imbalance issues, neural networks encounter challenges in accurately modeling the one-hot encoded curve stratification positions, resulting in significant deviations between predicted and actual stratification positions. Addressing these challenges, this study proposes a novel well logging curve stratigraphic comparison algorithm based on uniformly distributed soft labels. In the training phase, a label smoothing loss function is introduced to comprehensively account for the substantial loss stemming from data imbalance and to consider the similarity between different layer data. Concurrently, spatial attention and channel attention mechanisms are incorporated into the shallow and deep encoder stages of U²-Net, respectively, to better focus on changes in stratification positions. During the prediction phase, an optimized confidence threshold algorithm is proposed to constrain stratification results and solve the problem of reduced prediction accuracy because of occasional layer repetition. The proposed method is applied to real-world well logging data in oil fields. Quantitative evaluation results demonstrate that within error ranges of 1, 2, and 3 m, the accuracy of well logging curve stratigraphic division reaches 87.27%, 92.68%, and 95.08%, respectively, thus validating the effectiveness of the algorithm presented in this paper.